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3D-aware face generators are typically trained on 2D real-life face imagedatasets that primarily consist of near-frontal face data, and as such, theyare unable to construct one-quarter headshot 3D portraits with complete head,neck, and shoulder geometry. Two reasons account for this issue: First,existing facial recognition methods struggle with extracting facial datacaptured from large camera angles or back views. Second, it is challenging tolearn a distribution of 3D portraits covering the one-quarter headshot regionfrom single-view data due to significant geometric deformation caused bydiverse body poses. To this end, we first create the dataset360{\deg}-Portrait-HQ (360{\deg}PHQ for short) which consists of high-qualitysingle-view real portraits annotated with a variety of camera parameters (theyaw angles span the entire 360{\deg} range) and body poses. We then propose3DPortraitGAN, the first 3D-aware one-quarter headshot portrait generator thatlearns a canonical 3D avatar distribution from the 360{\deg}PHQ dataset withbody pose self-learning. Our model can generate view-consistent portrait imagesfrom all camera angles with a canonical one-quarter headshot 3D representation.Our experiments show that the proposed framework can accurately predictportrait body poses and generate view-consistent, realistic portrait imageswith complete geometry from all camera angles.

3DPortraitGAN: Learning One-Quarter Headshot 3D GANs from a Single-View Portrait Dataset with Diverse Body Poses